Disease is generally diagnosed based on a myriad of factors, both objective and subjective, including but not limited to symptoms, laboratory test values, demographic factors and environmental factors. Diagnosis relies on a clinician such as a physician or a veterinarian being able to identify and evaluate the relevant factors. Often this task can be difficult, and becomes exceedingly more so as the number of factors to be considered increases.
An example of a disease whose diagnosis is difficult is tumors. Tumors are currently diagnosed on the basis of clinical presentation, routine histology, immunohistochemistry and electron microscopy. However the histological appearance may not reveal the genetic aberrations or underlying biologic processes that contribute to the malignancy. Monitoring global gene expression levels using DNA microarrays would provide an additional tool for elucidating tumor biology as well as the potential for molecular diagnostic classification of cancers. Several studies have demonstrated that gene expression profiling using DNA microarrays is able to classify tumors with a high accuracy, and discover new cancer classes.
A specific type of tumors which could benefit is the small, round blue cell tumors (SRBCTs) of childhood as a model. SRBCTs include, neuroblastoma (NB), rhabdomyosarcoma (RMS), non-Hodgkin lymphoma (NHL) and the Ewing family of tumors (EWS), are so named because of their similar appearance on routine histology. However, accurate diagnosis of SRBCTs is essential because the treatment options, responses to therapy, and prognoses vary widely depending on the diagnosis. As their name implies, these cancers are difficult to distinguish by light microscopy, and currently no single test can precisely distinguish these cancers.
In clinical practice, several techniques are used for diagnosis, including immunohistochemistry, cytogenetics, interphase fluorescence in situ hybridization and reverse transcription (RT)-PCR. Immunohistochemistry allows the detection of protein expression, but it can only examine one protein at a time. Molecular techniques such as RT-PCR are used increasingly for diagnostic confirmation following the discovery of tumor-specific translocations such as EWS-FLI1; t(11;22)(q24;q12) in EWS, and the PAX3-FKHR; t(2;13)(q35;q14) in alveolar rhabdomyosarcoma (ARMS). However, molecular markers do not always provide a definitive diagnosis, as on occasion there is failure to detect the classical translocations, due to either technical difficulties or the presence of variant translocations.
An example of a diagnostic method replete with such problems is the diagnostic method for Ewing sarcoma. Ewing sarcoma is diagnosed by immunohistochemical evidence of MIC2 expression and lack of expression of the leukocyte common antigen CD45 (excluding lymphoma), muscle-specific actin or myogenin (excluding RMS). However, reliance on detection of MIC2 alone can lead to incorrect diagnosis as MIC2 expression occurs occasionally in other tumor types including RMS and NHL.
One objective factor that can, in certain circumstances, be entirely predictive of a diseased state is the genetic makeup of the individual. Genetic makeup of an individual can also be considered in terms of the level of expression of the genes of that individual through gene expression data.
DNA microarray technology is a recently developed high throughput technology for monitoring gene expression at the transcription level. Its use is akin to performing tens of thousands of northern blots simultaneously, and has the potential for parallel integration of the expression levels of an entire genome. A DNA microarray consists of DNA probes immobilized on a solid support such as a glass microscope slide. The DNA probes can be double stranded cDNA or short (25 mers) or long (50-70 mers) oligonucleotides of known sequences. An ideal DNA microarray should be able to interrogate all of the genes expressed in an organism.
In DNA microarrays using cDNA, the probes are PCR amplified from plasmid cDNA clones that have been purified and robotically printed onto coated glass slides. DNA microarrays using oligonucleotide have an advantage over cDNA microarrays because physical clones are not necessary. The oligonucleotides can either be previously synthesized and printed on glass slides, or can be synthesized directly on the surface of silicon or glass slides. Several print-ready oligonucleotide (60-70 men) sets are commercially available for human, mouse and other organisms (cgen/com.operon/com).
Another technique for fabricating oligonucleotides microarrays chemically synthesizes the oligonucleotides (25 mers) on a silicon surface using photolithography techniques. (Affymetrix Inc., Santa Clara, Calif.). Originally such arrays were designed to detect single-nucleotide mutations, but now have applications for gene expression profiling studies. Yet another technique delivers single nucleic acids, which ultimately form longer oligonucleotides (60 mers), by ink-jet onto glass surfaces.
One method of utilizing gene expression data from microarrays is given by Tusher et al., PNAS 98(9) p. 5116-21, April, 2001. The method of Tusher et al. is a statistical method titled Significance Analysis of Microarrays (“SAM”). The general approach in SAM is based on commonly used statistical tests, t-tests specifically, to find genes that discriminate between two classes in a gene-by-gene fashion. SAM uses replication of experiments to assign a significance to the discriminating genes in terms of a false discover rate. SAM therefore offers a method of choosing particular genes from a set of gene expression data, but does not offer a diagnosis based on those genes.
DNA microarrays would be an invaluable tool for disease diagnosis. Gene-expression profiling using DNA microarrays permits a simultaneous analysis of multiple markers, and can be used for example to categorize cancers into subgroups. The only limitation associated with the use of DNA microarrays is the vast amount of data generated thereby. A method that would allow for the easy and automated use of DNA microarray data in disease diagnosis is therefore desirable. Despite the many statistical techniques to analyze gene-expression data, none so far has been rigorously tested for their ability to accurately distinguish diseases belonging to several diagnostic categories. Such methods have also not been used to extract the genes or features that are the most important for the classification performance. Such genes would also generally be those that are of use to biologists and physicians as offering avenues to research in investigating cures.
Therefore, there remains a need for a method of using gene expression data to diagnose, predict, or prognosticate about a disease condition.
However, these other methods have not been used to extract the genes or features that are most important for the classification performance and which also will be of interest to cancer biologists.